@inproceedings{dddf7d9b4b54406aaa59a8099da35548,
title = "Seizure prediction using cross-correlation and classification",
abstract = "Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients. Cross-correlation coefficients are extracted every 2 seconds using a 4-second window with 50% overlap from focus electrodes identified by the epileptologist. Features are further processed by a second-order Kalman filter and then input to three different classifiers which include AdaBoost, radial basis function kernel support vector machine (RBF-SVM) and artificial neural network (ANN). The algorithm is tested on the long-term intra-cranial EEG (iEEG) database collected at the UMN epilepsy clinic. This database includes EEG recordings from 2 patients sampled from varying number of electrodes sampled at 2kHz. It is shown that the proposed algorithm achieves a high sensitivity and a low false positive rate.",
author = "Zisheng Zhang and Henry, {Thomas R.} and Parhi, {Keshab K.}",
year = "2016",
month = feb,
day = "26",
doi = "10.1109/ACSSC.2015.7421239",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "775--779",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015",
note = "49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 08-11-2015 Through 11-11-2015",
}